Post
214
The small projection-based approximator model for the geolip patchwork did not breach a certain level of accuracy as required by my specifications, so I've defaulted to harvesting direct geometric information from AI models until I get the comparative bounds required for a useful topology.
I must sincerely apologize for not solving this problem quickly.
This will take time. Without the approximator it's going to be considerably slower, but this model I begin training will be providing the approximations in a different way over time. As iterations progress, the system will conform to a huge array of geometric potentials and be capable at predicting those, but it will not be as powerful as the full patchmaker up front, and it will be slow training.
If I can get my hands on a cluster of A100's or H100's for a measure I'll make a post immediately, until then I must default to the slower process.
I really banked that the smaller version would have worked, but it simply couldn't hold complex topological shape without the correct boundaries being learnable AND endure entropic decay simultaneously. The only way to have a predominant shot at a full geometric shared language, is to make those boundaries learnable in the full spectrum of potentials, or at least more than I have placed on it.
I'll be refining my process in the coming days further, and I do apologize for pre-emptively announcing a potential that I have yet to fully explore.
There will be a full upgraded 38 shape geolip patchwork trained asap to fully encompass the Flux 1 AE spectrum, and another trained for SD15, SDXL, and Flux 2's VAE as well. These will accommodate DIRECT complex geometric patchwork learning, but not to the scale as promised yet. Autoregression is a complex mistress as many of you know, and I will be spending a great deal of time and compute analyzing all of the information required to build a uniformly useful and powerful autoregression patchwork to utilize as invariance to teaching.
I must sincerely apologize for not solving this problem quickly.
This will take time. Without the approximator it's going to be considerably slower, but this model I begin training will be providing the approximations in a different way over time. As iterations progress, the system will conform to a huge array of geometric potentials and be capable at predicting those, but it will not be as powerful as the full patchmaker up front, and it will be slow training.
If I can get my hands on a cluster of A100's or H100's for a measure I'll make a post immediately, until then I must default to the slower process.
I really banked that the smaller version would have worked, but it simply couldn't hold complex topological shape without the correct boundaries being learnable AND endure entropic decay simultaneously. The only way to have a predominant shot at a full geometric shared language, is to make those boundaries learnable in the full spectrum of potentials, or at least more than I have placed on it.
I'll be refining my process in the coming days further, and I do apologize for pre-emptively announcing a potential that I have yet to fully explore.
There will be a full upgraded 38 shape geolip patchwork trained asap to fully encompass the Flux 1 AE spectrum, and another trained for SD15, SDXL, and Flux 2's VAE as well. These will accommodate DIRECT complex geometric patchwork learning, but not to the scale as promised yet. Autoregression is a complex mistress as many of you know, and I will be spending a great deal of time and compute analyzing all of the information required to build a uniformly useful and powerful autoregression patchwork to utilize as invariance to teaching.
